Welcome to Covid19 Data Analysis and Visualization

---

Let's Import the modules

import pandas as pd 
import numpy as np 
import seaborn as sns
import matplotlib.pyplot as plt 
import plotly.express as px ### for plotting the data on world map
print('Modules are imported.')

Modules are imported.

Task 2

Task 2.1: importing covid19 dataset

importing "Covid19_Confirmed_dataset.csv" from "./Dataset" folder.

corona_dataset_csv = pd.read_csv("Datasets/covid19_Confirmed_dataset.csv")
corona_dataset_csv.head(10)

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	4/21/20	4/22/20	4/23/20	4/24/20	4/25/20	4/26/20	4/27/20	4/28/20	4/29/20	4/30/20
0	NaN	Afghanistan	33.0000	65.0000	0	0	0	0	0	0	...	1092	1176	1279	1351	1463	1531	1703	1828	1939	2171
1	NaN	Albania	41.1533	20.1683	0	0	0	0	0	0	...	609	634	663	678	712	726	736	750	766	773
2	NaN	Algeria	28.0339	1.6596	0	0	0	0	0	0	...	2811	2910	3007	3127	3256	3382	3517	3649	3848	4006
3	NaN	Andorra	42.5063	1.5218	0	0	0	0	0	0	...	717	723	723	731	738	738	743	743	743	745
4	NaN	Angola	-11.2027	17.8739	0	0	0	0	0	0	...	24	25	25	25	25	26	27	27	27	27
5	NaN	Antigua and Barbuda	17.0608	-61.7964	0	0	0	0	0	0	...	23	24	24	24	24	24	24	24	24	24
6	NaN	Argentina	-38.4161	-63.6167	0	0	0	0	0	0	...	3031	3144	3435	3607	3780	3892	4003	4127	4285	4428
7	NaN	Armenia	40.0691	45.0382	0	0	0	0	0	0	...	1401	1473	1523	1596	1677	1746	1808	1867	1932	2066
8	Australian Capital Territory	Australia	-35.4735	149.0124	0	0	0	0	0	0	...	104	104	104	105	106	106	106	106	106	106
9	New South Wales	Australia	-33.8688	151.2093	0	0	0	0	3	4	...	2969	2971	2976	2982	2994	3002	3004	3016	3016	3025

10 rows × 104 columns

Let's check the shape of the dataframe

corona_dataset_csv.shape

(266, 104)

Task 2.2: Delete the useless columns

corona_dataset_csv.drop(["Lat", "Long"], axis = 1, inplace = True)

corona_dataset_csv.head(10)

	Province/State	Country/Region	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	...	4/21/20	4/22/20	4/23/20	4/24/20	4/25/20	4/26/20	4/27/20	4/28/20	4/29/20	4/30/20
0	NaN	Afghanistan	0	0	0	0	0	0	0	0	...	1092	1176	1279	1351	1463	1531	1703	1828	1939	2171
1	NaN	Albania	0	0	0	0	0	0	0	0	...	609	634	663	678	712	726	736	750	766	773
2	NaN	Algeria	0	0	0	0	0	0	0	0	...	2811	2910	3007	3127	3256	3382	3517	3649	3848	4006
3	NaN	Andorra	0	0	0	0	0	0	0	0	...	717	723	723	731	738	738	743	743	743	745
4	NaN	Angola	0	0	0	0	0	0	0	0	...	24	25	25	25	25	26	27	27	27	27
5	NaN	Antigua and Barbuda	0	0	0	0	0	0	0	0	...	23	24	24	24	24	24	24	24	24	24
6	NaN	Argentina	0	0	0	0	0	0	0	0	...	3031	3144	3435	3607	3780	3892	4003	4127	4285	4428
7	NaN	Armenia	0	0	0	0	0	0	0	0	...	1401	1473	1523	1596	1677	1746	1808	1867	1932	2066
8	Australian Capital Territory	Australia	0	0	0	0	0	0	0	0	...	104	104	104	105	106	106	106	106	106	106
9	New South Wales	Australia	0	0	0	0	3	4	4	4	...	2969	2971	2976	2982	2994	3002	3004	3016	3016	3025

10 rows × 102 columns

Task 2.3: Aggregating the rows by the country

corona_dataset_aggregated = corona_dataset_csv.groupby("Country/Region").sum()

corona_dataset_aggregated.head()

	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	1/30/20	1/31/20	...	4/21/20	4/22/20	4/23/20	4/24/20	4/25/20	4/26/20	4/27/20	4/28/20	4/29/20	4/30/20
Country/Region
Afghanistan	0	0	0	0	0	0	0	0	0	0	...	1092	1176	1279	1351	1463	1531	1703	1828	1939	2171
Albania	0	0	0	0	0	0	0	0	0	0	...	609	634	663	678	712	726	736	750	766	773
Algeria	0	0	0	0	0	0	0	0	0	0	...	2811	2910	3007	3127	3256	3382	3517	3649	3848	4006
Andorra	0	0	0	0	0	0	0	0	0	0	...	717	723	723	731	738	738	743	743	743	745
Angola	0	0	0	0	0	0	0	0	0	0	...	24	25	25	25	25	26	27	27	27	27

5 rows × 100 columns

corona_dataset_aggregated.shape

(187, 100)

Task 2.4: Visualizing data related to a country for example China

visualization always helps for better understanding of our data.

corona_dataset_aggregated.loc["China"].plot()
corona_dataset_aggregated.loc["Italy"].plot()
corona_dataset_aggregated.loc["Spain"].plot()
corona_dataset_aggregated.loc["Canada"].plot()
plt.legend()

<matplotlib.legend.Legend at 0x1a20163d10>

Task3: Calculating a good measure

we need to find a good measure reperestend as a number, describing the spread of the virus in a country.

corona_dataset_aggregated.loc['Canada'].plot()

<AxesSubplot:>

corona_dataset_aggregated.loc["Canada"][:3].plot()

<AxesSubplot:>

task 3.1: caculating the first derivative of the curve

corona_dataset_aggregated.loc["Canada"].diff().plot()

<AxesSubplot:>

task 3.2: find maxmimum infection rate for China

corona_dataset_aggregated.loc["Canada"].diff().max()

2778.0

corona_dataset_aggregated.loc["Canada"].diff().max()

2778.0

corona_dataset_aggregated.loc["Spain"].diff().max()

9630.0

Task 3.3: find maximum infection rate for all of the countries.

countries = list(corona_dataset_aggregated.index)
max_infection_rates = []
for c in countries :
    max_infection_rates.append(corona_dataset_aggregated.loc[c].diff().max())
max_infection_rates

[232.0,
 34.0,
 199.0,
 43.0,
 5.0,
 6.0,
 291.0,
 134.0,
 497.0,
 1321.0,
 105.0,
 7.0,
 301.0,
 641.0,
 12.0,
 1485.0,
 2454.0,
 4.0,
 19.0,
 1.0,
 104.0,
 92.0,
 7.0,
 7502.0,
 26.0,
 137.0,
 41.0,
 21.0,
 6.0,
 45.0,
 31.0,
 203.0,
 2778.0,
 31.0,
 21.0,
 1138.0,
 15136.0,
 353.0,
 1.0,
 57.0,
 81.0,
 37.0,
 113.0,
 96.0,
 63.0,
 58.0,
 381.0,
 391.0,
 99.0,
 156.0,
 5.0,
 371.0,
 11536.0,
 269.0,
 32.0,
 130.0,
 7.0,
 134.0,
 20.0,
 9.0,
 5.0,
 267.0,
 26849.0,
 38.0,
 5.0,
 42.0,
 6933.0,
 403.0,
 156.0,
 6.0,
 68.0,
 167.0,
 132.0,
 12.0,
 10.0,
 3.0,
 72.0,
 210.0,
 99.0,
 1893.0,
 436.0,
 3186.0,
 91.0,
 1515.0,
 1131.0,
 6557.0,
 52.0,
 1161.0,
 40.0,
 264.0,
 29.0,
 851.0,
 289.0,
 300.0,
 69.0,
 3.0,
 48.0,
 61.0,
 17.0,
 13.0,
 21.0,
 90.0,
 234.0,
 7.0,
 14.0,
 10.0,
 235.0,
 190.0,
 58.0,
 52.0,
 2.0,
 41.0,
 1425.0,
 222.0,
 12.0,
 13.0,
 30.0,
 281.0,
 19.0,
 3.0,
 14.0,
 1346.0,
 89.0,
 2.0,
 69.0,
 208.0,
 107.0,
 386.0,
 144.0,
 1292.0,
 357.0,
 5.0,
 27.0,
 3683.0,
 538.0,
 545.0,
 1516.0,
 957.0,
 523.0,
 7099.0,
 22.0,
 5.0,
 6.0,
 4.0,
 54.0,
 6.0,
 1351.0,
 87.0,
 2379.0,
 2.0,
 20.0,
 1426.0,
 114.0,
 70.0,
 73.0,
 354.0,
 28.0,
 9630.0,
 65.0,
 67.0,
 3.0,
 812.0,
 1321.0,
 6.0,
 27.0,
 15.0,
 181.0,
 188.0,
 10.0,
 14.0,
 40.0,
 82.0,
 5138.0,
 36188.0,
 11.0,
 578.0,
 552.0,
 8733.0,
 48.0,
 167.0,
 29.0,
 19.0,
 66.0,
 4.0,
 5.0,
 9.0,
 8.0]

countries = list(corona_dataset_aggregated.index)
max_infection_rates = []
for c in countries :
    max_infection_rates.append(corona_dataset_aggregated.loc[c].diff().max())
corona_dataset_aggregated["max_infection_rates"] = max_infection_rates

corona_dataset_aggregated.head()

	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	1/28/20	1/29/20	1/30/20	1/31/20	...	4/22/20	4/23/20	4/24/20	4/25/20	4/26/20	4/27/20	4/28/20	4/29/20	4/30/20	max_infection_rates
Country/Region
Afghanistan	0	0	0	0	0	0	0	0	0	0	...	1176	1279	1351	1463	1531	1703	1828	1939	2171	232.0
Albania	0	0	0	0	0	0	0	0	0	0	...	634	663	678	712	726	736	750	766	773	34.0
Algeria	0	0	0	0	0	0	0	0	0	0	...	2910	3007	3127	3256	3382	3517	3649	3848	4006	199.0
Andorra	0	0	0	0	0	0	0	0	0	0	...	723	723	731	738	738	743	743	743	745	43.0
Angola	0	0	0	0	0	0	0	0	0	0	...	25	25	25	25	26	27	27	27	27	5.0

5 rows × 101 columns

Task 3.4: create a new dataframe with only needed column

corona_data = pd.DataFrame(corona_dataset_aggregated["max_infection_rates"])

corona_data.head()

	max_infection_rates
Country/Region
Afghanistan	232.0
Albania	34.0
Algeria	199.0
Andorra	43.0
Angola	5.0

Task4:

• Importing the WorldHappinessReport.csv dataset
• selecting needed columns for our analysis
• join the datasets
• calculate the correlations as the result of our analysis

Task 4.1 : importing the dataset

happiness_report_csv = pd.read_csv("Datasets/worldwide_happiness_report.csv")

happiness_report_csv.head()

	Overall rank	Country or region	Score	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption
0	1	Finland	7.769	1.340	1.587	0.986	0.596	0.153	0.393
1	2	Denmark	7.600	1.383	1.573	0.996	0.592	0.252	0.410
2	3	Norway	7.554	1.488	1.582	1.028	0.603	0.271	0.341
3	4	Iceland	7.494	1.380	1.624	1.026	0.591	0.354	0.118
4	5	Netherlands	7.488	1.396	1.522	0.999	0.557	0.322	0.298

Task 4.2: let's drop the useless columns

useless_cols = ["Overall rank", "Score", "Generosity", "Perceptions of corruption"]

happiness_report_csv.drop(useless_cols, axis = 1, inplace = True)
happiness_report_csv.head()

	Country or region	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices
0	Finland	1.340	1.587	0.986	0.596
1	Denmark	1.383	1.573	0.996	0.592
2	Norway	1.488	1.582	1.028	0.603
3	Iceland	1.380	1.624	1.026	0.591
4	Netherlands	1.396	1.522	0.999	0.557

Task 4.3: changing the indices of the dataframe

# put the name  of the countries as the index
happiness_report_csv.set_index("Country or region", inplace = True)

happiness_report_csv.head()

	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices
Country or region
Finland	1.340	1.587	0.986	0.596
Denmark	1.383	1.573	0.996	0.592
Norway	1.488	1.582	1.028	0.603
Iceland	1.380	1.624	1.026	0.591
Netherlands	1.396	1.522	0.999	0.557

Task4.4: now let's join two dataset we have prepared

Corona Dataset :

corona_data.head()

	max_infection_rates
Country/Region
Afghanistan	232.0
Albania	34.0
Algeria	199.0
Andorra	43.0
Angola	5.0

corona_data.shape

(187, 1)

wolrd happiness report Dataset :

happiness_report_csv.head()

	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices
Country or region
Finland	1.340	1.587	0.986	0.596
Denmark	1.383	1.573	0.996	0.592
Norway	1.488	1.582	1.028	0.603
Iceland	1.380	1.624	1.026	0.591
Netherlands	1.396	1.522	0.999	0.557

happiness_report_csv.shape

(156, 4)

#we need to used inner joing
data = corona_data.join(happiness_report_csv, how ="inner")
data.head()

	max_infection_rates	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices
Afghanistan	232.0	0.350	0.517	0.361	0.000
Albania	34.0	0.947	0.848	0.874	0.383
Algeria	199.0	1.002	1.160	0.785	0.086
Argentina	291.0	1.092	1.432	0.881	0.471
Armenia	134.0	0.850	1.055	0.815	0.283

Task 4.5: correlation matrix

#see the correlation between the diffferent columns
data.corr()

	max_infection_rates	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices
max_infection_rates	1.000000	0.250118	0.191958	0.289263	0.078196
GDP per capita	0.250118	1.000000	0.759468	0.863062	0.394603
Social support	0.191958	0.759468	1.000000	0.765286	0.456246
Healthy life expectancy	0.289263	0.863062	0.765286	1.000000	0.427892
Freedom to make life choices	0.078196	0.394603	0.456246	0.427892	1.000000

The higher the correlation values, the higher the correlation between those two columns. We can see the factors that correspond to maximum infection rate.

Task 5: Visualization of the results

our Analysis is not finished unless we visualize the results in terms figures and graphs so that everyone can understand what you get out of our analysis

data.head()

	max_infection_rates	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices
Afghanistan	232.0	0.350	0.517	0.361	0.000
Albania	34.0	0.947	0.848	0.874	0.383
Algeria	199.0	1.002	1.160	0.785	0.086
Argentina	291.0	1.092	1.432	0.881	0.471
Armenia	134.0	0.850	1.055	0.815	0.283

Task 5.1: Plotting GDP vs maximum Infection rate

x = data["GDP per capita"]
y = data["max_infection_rates"]
sns.scatterplot(x=x,y=y)

<AxesSubplot:xlabel='GDP per capita', ylabel='max_infection_rates'>

x = data["GDP per capita"]
y = data["max_infection_rates"]
sns.scatterplot(x=x,y=np.log(y))

<AxesSubplot:xlabel='GDP per capita', ylabel='max_infection_rates'>

sns.regplot(x=x, y=np.log(y))

<AxesSubplot:xlabel='GDP per capita', ylabel='max_infection_rates'>

Task 5.2: Plotting Social support vs maximum Infection rate

x = data["Social support"]
y = data["max_infection_rates"]
sns.scatterplot(x=x,y=np.log(y))

<AxesSubplot:xlabel='Social support', ylabel='max_infection_rates'>

sns.regplot(x=x, y=np.log(y))

<AxesSubplot:xlabel='Social support', ylabel='max_infection_rates'>

Task 5.3: Plotting Healthy life expectancy vs maximum Infection rate

x = data["Healthy life expectancy"]
y = data["max_infection_rates"]
sns.scatterplot(x=x,y=np.log(y))

<AxesSubplot:xlabel='Healthy life expectancy', ylabel='max_infection_rates'>

sns.regplot(x=x, y=np.log(y))

<AxesSubplot:xlabel='Healthy life expectancy', ylabel='max_infection_rates'>

Task 5.4: Plotting Freedom to make life choices vs maximum Infection rate

x = data['Freedom to make life choices']
y = data['max_infection_rates']
sns.scatterplot(x=x,y=np.log(y))

<AxesSubplot:xlabel='Freedom to make life choices', ylabel='max_infection_rates'>

sns.regplot(x=x,y=np.log(y))

<AxesSubplot:xlabel='Freedom to make life choices', ylabel='max_infection_rates'>

Visualizing Daily Covid 19 Report using May 25 and November 8, 2020 Dataset

These visualizations are based on data as of May 25, and November 8, 2020. I have used the daily report data published by John Hopkins University for May 25, 2020. The next part of the code deals with loading the .csv data to our project.

#load data
df = pd.read_csv("Datasets/05-25-2020.csv")
df.info()
df.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3410 entries, 0 to 3409
Data columns (total 12 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   FIPS            3006 non-null   float64
 1   Admin2          3009 non-null   object 
 2   Province_State  3232 non-null   object 
 3   Country_Region  3410 non-null   object 
 4   Last_Update     3410 non-null   object 
 5   Lat             3341 non-null   float64
 6   Long_           3341 non-null   float64
 7   Confirmed       3410 non-null   int64  
 8   Deaths          3410 non-null   int64  
 9   Recovered       3410 non-null   int64  
 10  Active          3410 non-null   int64  
 11  Combined_Key    3410 non-null   object 
dtypes: float64(3), int64(4), object(5)
memory usage: 319.8+ KB

	FIPS	Admin2	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Recovered	Active	Combined_Key
0	45001.0	Abbeville	South Carolina	US	2020-05-26 02:32:35	34.223334	-82.461707	35	0	0	35	Abbeville, South Carolina, US
1	22001.0	Acadia	Louisiana	US	2020-05-26 02:32:35	30.295065	-92.414197	299	18	0	281	Acadia, Louisiana, US
2	51001.0	Accomack	Virginia	US	2020-05-26 02:32:35	37.767072	-75.632346	731	11	0	720	Accomack, Virginia, US
3	16001.0	Ada	Idaho	US	2020-05-26 02:32:35	43.452658	-116.241552	796	23	0	773	Ada, Idaho, US
4	19001.0	Adair	Iowa	US	2020-05-26 02:32:35	41.330756	-94.471059	8	0	0	8	Adair, Iowa, US

Preprocessing the data

Now since our data has loaded successfully, the next step is to preprocess the data before using it for plotting. It will include :

• Removing superfluous columns like ‘FIPS’, ‘Admin2', ‘Last_Update’ (since all the data is for single-day — 25th May).
• Removing columns ‘Province_State’ and ‘Combined_Key’ since statewide data is not available for all the countries
• Grouping together data by ‘Country_Region’ and rename the column to ‘Country’

df.drop(['FIPS', 'Admin2','Last_Update','Province_State', 'Combined_Key'], axis=1, inplace=True)
df.rename(columns={'Country_Region': "Country"}, inplace=True)
df.head()

	Country	Lat	Long_	Confirmed	Deaths	Recovered	Active
0	US	34.223334	-82.461707	35	0	0	35
1	US	30.295065	-92.414197	299	18	0	281
2	US	37.767072	-75.632346	731	11	0	720
3	US	43.452658	-116.241552	796	23	0	773
4	US	41.330756	-94.471059	8	0	0	8

The data can be grouped together by the ‘groupby’ function of the dataframe. It is similar to the GROUPBY statement in SQL.

### group the data by country

world = df.groupby("Country")['Confirmed','Active','Recovered','Deaths'].sum().reset_index()
world.head()

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  This is separate from the ipykernel package so we can avoid doing imports until

	Country	Confirmed	Active	Recovered	Deaths
0	Afghanistan	11173	9857	1097	219
1	Albania	1004	177	795	32
2	Algeria	8503	3147	4747	609
3	Andorra	763	49	663	51
4	Angola	70	48	18	4

Plotting the top 20 countries with the maximum number of confirmed cases

### Find top 20 countries with maximum number of confirmed cases
top_20 = world.sort_values(by=['Confirmed'], ascending=False).head(20)
### Generate a Barplot
plt.figure(figsize=(12,10))
plot = sns.barplot(top_20['Confirmed'], top_20['Country'])
for i,(value,name) in enumerate(zip(top_20['Confirmed'],top_20['Country'])):
  plot.text(value,i-0.05,f'{value:,.0f}',size=10)
plt.show()

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

Plotting Confirmed and Active cases for the top 5 countries with the maximum number of confirmed cases

top_10 = world.sort_values(by=['Confirmed'], ascending=False).head(10)

### Generate a Barplot
plt.figure(figsize=(15,10))
confirmed = sns.barplot(top_10['Confirmed'], top_10['Country'], color = 'red', label='Confirmed')
recovered = sns.barplot(top_10['Recovered'], top_10['Country'], color = 'green', label='Recovered')

### Adding Texts for barplots
for i,(value,name) in enumerate(zip(top_10['Confirmed'],top_10['Country'])):
  confirmed.text(value,i-0.05,f'{value:,.0f}',size=9)
for i,(value,name) in enumerate(zip(top_10['Recovered'],top_10['Country'])):
  recovered.text(value,i-0.05,f'{value:,.0f}',size=9)
plt.legend(loc=4)
plt.show()

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning
/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

Plotting a Choropleth map on World Map

• A choropleth map is a type of thematic map in which areas are shaded or patterned in proportion to a statistical variable that represents an aggregate summary of a geographic characteristic within each area, such as population density or per-capita income.

• Choropleth maps provide an easy way to visualize how a measurement varies across a geographic area or show the level of variability within a region

figure = px.choropleth(world,locations='Country', locationmode='country names', color='Confirmed', hover_name='Country', color_continuous_scale='tealgrn', range_color=[1,1000000],title='Countries with Confirmed cases')
figure.show()

As of November 8, 2020

#Read data
df = pd.read_csv("Datasets/11-08-2020.csv")
df.info()
df.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3960 entries, 0 to 3959
Data columns (total 14 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   FIPS                 3262 non-null   float64
 1   Admin2               3267 non-null   object 
 2   Province_State       3790 non-null   object 
 3   Country_Region       3960 non-null   object 
 4   Last_Update          3960 non-null   object 
 5   Lat                  3878 non-null   float64
 6   Long_                3878 non-null   float64
 7   Confirmed            3960 non-null   int64  
 8   Deaths               3960 non-null   int64  
 9   Recovered            3960 non-null   int64  
 10  Active               3958 non-null   float64
 11  Combined_Key         3960 non-null   object 
 12  Incidence_Rate       3878 non-null   float64
 13  Case-Fatality_Ratio  3917 non-null   float64
dtypes: float64(6), int64(3), object(5)
memory usage: 433.2+ KB

	FIPS	Admin2	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Recovered	Active	Combined_Key	Incidence_Rate	Case-Fatality_Ratio
0	NaN	NaN	NaN	Afghanistan	2020-11-09 05:25:45	33.93911	67.709953	42092	1558	34458	6076.0	Afghanistan	108.126879	3.701416
1	NaN	NaN	NaN	Albania	2020-11-09 05:25:45	41.15330	20.168300	24206	559	12092	11555.0	Albania	841.128640	2.309345
2	NaN	NaN	NaN	Algeria	2020-11-09 05:25:45	28.03390	1.659600	62051	2048	42037	17966.0	Algeria	141.504046	3.300511
3	NaN	NaN	NaN	Andorra	2020-11-09 05:25:45	42.50630	1.521800	5383	75	4248	1060.0	Andorra	6966.931987	1.393275
4	NaN	NaN	NaN	Angola	2020-11-09 05:25:45	-11.20270	17.873900	12433	307	5899	6227.0	Angola	37.829059	2.469235

df.drop(['FIPS', 'Admin2','Last_Update','Province_State', 'Combined_Key'], axis=1, inplace=True)
df.rename(columns={'Country_Region': "Country"}, inplace=True)
df.head()

	Country	Lat	Long_	Confirmed	Deaths	Recovered	Active	Incidence_Rate	Case-Fatality_Ratio
0	Afghanistan	33.93911	67.709953	42092	1558	34458	6076.0	108.126879	3.701416
1	Albania	41.15330	20.168300	24206	559	12092	11555.0	841.128640	2.309345
2	Algeria	28.03390	1.659600	62051	2048	42037	17966.0	141.504046	3.300511
3	Andorra	42.50630	1.521800	5383	75	4248	1060.0	6966.931987	1.393275
4	Angola	-11.20270	17.873900	12433	307	5899	6227.0	37.829059	2.469235

### group the data by country

world = df.groupby("Country")['Confirmed','Active','Recovered','Deaths'].sum().reset_index()
world.head()

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning:

Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.

	Country	Confirmed	Active	Recovered	Deaths
0	Afghanistan	42092	6076.0	34458	1558
1	Albania	24206	11555.0	12092	559
2	Algeria	62051	17966.0	42037	2048
3	Andorra	5383	1060.0	4248	75
4	Angola	12433	6227.0	5899	307

### Find top 20 countries with maximum number of confirmed cases
top_20 = world.sort_values(by=['Confirmed'], ascending=False).head(20)
### Generate a Barplot
plt.figure(figsize=(12,10))
plot = sns.barplot(top_20['Confirmed'], top_20['Country'])
for i,(value,name) in enumerate(zip(top_20['Confirmed'],top_20['Country'])):
  plot.text(value,i-0.05,f'{value:,.0f}',size=10)
plt.show()

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

top_10 = world.sort_values(by=['Confirmed'], ascending=False).head(10)

### Generate a Barplot
plt.figure(figsize=(15,10))
confirmed = sns.barplot(top_10['Confirmed'], top_10['Country'], color = 'red', label='Confirmed')
recovered = sns.barplot(top_10['Recovered'], top_10['Country'], color = 'green', label='Recovered')

### Adding Texts for barplots
for i,(value,name) in enumerate(zip(top_10['Confirmed'],top_10['Country'])):
  confirmed.text(value,i-0.05,f'{value:,.0f}',size=9)
for i,(value,name) in enumerate(zip(top_10['Recovered'],top_10['Country'])):
  recovered.text(value,i-0.05,f'{value:,.0f}',size=9)
plt.legend(loc=4)
plt.show()

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

figure = px.choropleth(world,locations='Country', locationmode='country names', color='Confirmed', hover_name='Country', color_continuous_scale='tealgrn', range_color=[1,1000000],title='Countries with Confirmed cases')
figure.show()

figure = px.choropleth(world,locations='Country', locationmode='country names', color='Deaths',
                       hover_name='Country', color_continuous_scale='tealgrn', range_color=[1,1000000],title='Countries with Death cases')
figure.show()

figure = px.choropleth(world,locations='Country', locationmode='country names', color='Recovered', hover_name='Country', color_continuous_scale='tealgrn', range_color=[1,1000000],title='Countries with Recovered cases')
figure.show()

corona datase

datasets

Cases Over Time

full_grouped = pd.read_csv('Datasets/full_grouped.csv')
full_grouped['Date'] = pd.to_datetime(full_grouped['Date'])
full_grouped.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
0	2020-01-22	Afghanistan	0	0	0	0	0	0	0	Eastern Mediterranean
1	2020-01-22	Albania	0	0	0	0	0	0	0	Europe
2	2020-01-22	Algeria	0	0	0	0	0	0	0	Africa
3	2020-01-22	Andorra	0	0	0	0	0	0	0	Europe
4	2020-01-22	Angola	0	0	0	0	0	0	0	Africa

full_grouped.shape

(35156, 10)

# Over the time

fig = px.choropleth(full_grouped, locations="Country/Region", 
                    color=np.log(full_grouped["Confirmed"]),
                    locationmode='country names', hover_name="Country/Region", 
                    animation_frame=full_grouped["Date"].dt.strftime('%Y-%m-%d'),
                    title='Cases over time', color_continuous_scale=px.colors.sequential.matter)
fig.update(layout_coloraxis_showscale=False)
fig.show()

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/pandas/core/series.py:726: RuntimeWarning:

divide by zero encountered in log

Cases over the time

# color pallette
cnf, dth, rec, act = '#393e46', '#ff2e63', '#21bf73', '#fe9801'

temp = full_grouped.groupby('Date')['Recovered', 'Deaths', 'Active'].sum().reset_index()
temp = temp.melt(id_vars="Date", value_vars=['Recovered', 'Deaths', 'Active'],var_name='Case', value_name='Count')
temp.head()

fig = px.area(temp, x="Date", y="Count", color='Case', height=600, width=700,title='Cases over time', color_discrete_sequence = [rec, dth, act])
fig.update_layout(xaxis_rangeslider_visible=True)

fig.show()

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning:

Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.

def plot_stacked(col):
    fig = px.bar(full_grouped, x="Date", y=col, color='Country/Region',
                 height=600, title=col,
                 color_discrete_sequence = px.colors.cyclical.mygbm)
    fig.update_layout(showlegend=True)
    fig.show()

def plot_line(col):
    fig = px.line(full_grouped, x="Date", y=col, color='Country/Region',
                  height=600, title=col,color_discrete_sequence = px.colors.cyclical.mygbm)
    fig.update_layout(showlegend=True)
    fig.show()

plot_stacked('Confirmed')

plot_stacked('Deaths')

plot_stacked('New cases')

plot_stacked('Active')

plot_line('Confirmed')

plot_line('Deaths')

plot_line('New cases')

plot_line('Active')

Graph after 1M cases

"""
def gt_n(n):
    countries = full_grouped[full_grouped['Confirmed']>n]['Country/Region'].unique()
    temp = full_table[full_table['Country/Region'].isin(countries)]
    temp = temp.groupby(['Country/Region', 'Date'])['Confirmed'].sum().reset_index()
    temp = temp[temp['Confirmed']>n]
    # print(temp.head())
    min_date = temp.groupby('Country/Region')['Date'].min().reset_index()
    min_date.columns = ['Country/Region', 'Min Date']
    # print(min_date.head())
    from_nth_case = pd.merge(temp, min_date, on='Country/Region')
    from_nth_case['Date'] = pd.to_datetime(from_nth_case['Date'])
    from_nth_case['Min Date'] = pd.to_datetime(from_nth_case['Min Date'])
    from_nth_case['N days'] = (from_nth_case['Date'] - from_nth_case['MinDate']).dt.days
    # print(from_nth_case.head())
    fig = px.line(from_nth_case, x='N days', y='Confirmed', color='Country/Region',
                  title='N days from '+str(n)+' case', height=600)
    fig.show()
"""

"\ndef gt_n(n):\n    countries = full_grouped[full_grouped['Confirmed']>n]['Country/Region'].unique()\n    temp = full_table[full_table['Country/Region'].isin(countries)]\n    temp = temp.groupby(['Country/Region', 'Date'])['Confirmed'].sum().reset_index()\n    temp = temp[temp['Confirmed']>n]\n    # print(temp.head())\n    min_date = temp.groupby('Country/Region')['Date'].min().reset_index()\n    min_date.columns = ['Country/Region', 'Min Date']\n    # print(min_date.head())\n    from_nth_case = pd.merge(temp, min_date, on='Country/Region')\n    from_nth_case['Date'] = pd.to_datetime(from_nth_case['Date'])\n    from_nth_case['Min Date'] = pd.to_datetime(from_nth_case['Min Date'])\n    from_nth_case['N days'] = (from_nth_case['Date'] - from_nth_case['MinDate']).dt.days\n    # print(from_nth_case.head())\n    fig = px.line(from_nth_case, x='N days', y='Confirmed', color='Country/Region',\n                  title='N days from '+str(n)+' case', height=600)\n    fig.show()\n"

#gt_n(100000)

Bubble Plot

def plot_bubble(col, pal):
    temp = full_grouped[full_grouped[col]>0].sort_values('Country/Region',ascending=False)
    fig = px.scatter(temp, x='Date', y='Country/Region', size=col, color=col,height=3000,
                     color_continuous_scale=pal)
    fig.update_layout(yaxis = dict(dtick = 1))
    fig.update(layout_coloraxis_showscale=False)
    fig.show()

plot_bubble('New cases', 'Viridis')

plot_bubble('Active', 'Viridis')

Epidemic Span

temp = full_grouped[['Date', 'Country/Region', 'New cases']]
temp['New cases reported ?'] = temp['New cases']!=0
temp['New cases reported ?'] = temp['New cases reported ?'].astype(int)
# temp.head()

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

import plotly.graph_objs as go

fig = go.Figure(data=go.Heatmap(
    z=temp['New cases reported ?'],
    x=temp['Date'],
    y=temp['Country/Region'],
    colorscale='Emrld',
    showlegend=False,
    text=temp['New cases reported ?']))
fig.update_layout(yaxis = dict(dtick = 1))
fig.update_layout(height=3000)
fig.show()

Weekly Statistics

full_grouped['Week No.'] = full_grouped['Date'].dt.strftime('%U')
week_wise = full_grouped.groupby('Week No.')['Confirmed', 'Deaths',
                                             'Recovered', 'Active', 'New cases', 
                                             'New deaths', 'New recovered'].sum().reset_index()

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning:

Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.

def plot_weekwise(col, hue):
    fig = px.bar(week_wise, x="Week No.", y=col, width=700,color_discrete_sequence=[hue])
    fig.update_layout(title=col, xaxis_title="", yaxis_title="")
    fig.show()

plot_weekwise('Confirmed', '#000000')

plot_weekwise('Deaths', dth)

plot_weekwise('New cases', '#cd6684')

Monthly statistics

full_grouped['Month'] = pd.DatetimeIndex(full_grouped['Date']).month
month_wise = full_grouped.groupby('Month')['Confirmed', 'Deaths', 'Recovered',
                                           'Active', 'New cases', 'New deaths', 
                                           'New recovered'].sum().reset_index()

/Users/eamankwah/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning:

Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.

def plot_monthwise(col, hue):
    fig = px.bar(month_wise, x="Month", y=col, width=700,color_discrete_sequence=[hue])
    fig.update_layout(title=col, xaxis_title="", yaxis_title="")
    fig.show()

plot_monthwise('Confirmed', '#000000')

plot_monthwise('Deaths', dth)

plot_monthwise('New cases', '#cd6684')

credit:

Jaskeerat Singh Bhatia

Devakumar Kp